Stefano Castruccio

Stefano Castruccio is a Notre Dame Collegiate Associate Professor at the University of Notre Dame, specializing in environmental statistics and spatio-temporal modeling since joining in 2017. His research bridges statistical methodology with critical environmental applications including renewable energy assessment and air pollution impact analysis.

His educational background includes:

• Ph.D. in Statistics from the University of Chicago (2013)
• M.S. in Mathematical Engineering from Politecnico di Milano, Italy (2007)
• B.S. in Mathematical Engineering from Politecnico di Milano, Italy (2005)

Prof. Castruccio's research centers on developing computationally efficient spatio-temporal models for environmental systems, with major contributions in wind energy resource assessment and climate modeling. His methodological innovations integrate physics-informed machine learning with traditional statistical frameworks to address challenges in high-resolution data analysis and uncertainty quantification.

His recent publications (2018-2025) reveal a strategic evolution toward deep learning integration for wind modeling and physics-constrained statistical frameworks, significantly advancing computational approaches for renewable energy forecasting and environmental risk assessment.

His scientific recognition includes:

• Elected fellow of the American Statistical Association
• Elected member of the International Statistical Institute
• 2024 Gordon Bell Prize for Climate Modelling
• 2023 TIES President's invited lecture
• 2021 TIES Abdel El-Shaarawi Early Investigator Award
• 2020 Early Investigator Award from ASA Section on Statistics and the Environment

Research funding includes major grants from NSF, NASA, and KAUST, most recently a $240,000 NSF CDS&E-MSS award for physics-informed forecasting of high-resolution spatio-temporal data. He maintains active international collaborations with Politecnico di Milano and KAUST through his research group's GitHub repository.

His team focuses on open-source development of statistical tools for environmental applications, with ongoing projects in turbulent boundary layer modeling and high-resolution wind energy forecasting.